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Optimization based Artificial Neural Networks
Carlos J. Gmez-Mndez
University o Puerto Rico at Mayaguez
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Identify a system for which its mathematical model is
unknown Use Artificial Neural Networks (ANN) to model the
system
rain t e using artic e warm ptimization
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What is s stem identification?
Mathematical model Example:
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Why identify a system?
Complicated mathematical model Time consuming to develop the
mathematical model
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T es of mathematical models:
White Box Model
Black Box Model
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What is an ANN?
Mathematical model based on biological neural networks Why
ANN?
apta ty
Non-linear characteristics
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Model of a neuron:
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Structure:
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What is PSO?
Swarm Intelligence
Examples: Flocks of birds, School of fish
Why PSO?
Iterative
How it works?
Particles represent set of data
Example: X,Y space
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Target is (0,0): x
10 Particles Random initial positions and velocities
Global best error and local best errors taken into
consideration
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Necessary System data is measured
ANN and PSO parameters are initialized
Input data is fed to the ANN
Error is calculated
ANN is retrained using PSO Repeat until acceptable error is
achieved
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The transfer function of the system being modeled with an
ANN
is:
The in ut to the s stem is an unit ste :
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System response to unit step input and output of initialized
ANN:
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System response to unit step input and output of trained
ANN:
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The algorithm developed will be applied for maximum
power tracking of an ocean wave energy conversion system.
Preliminary simulations will be done in real-time using
.
The algorithms will be usedor power management o a
DC Motor. The setup consists
of a DC Motor, DC Generator,
Power Converter Board, DSP
Board, and PC.
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